Autonomous driving has quickly become an area of interest for vehicle manufacturers and navigation and mapping service providers. One particular area of interest is the use of computer vision to enable mapping and sensing of a vehicle's environment to support autonomous or semi-autonomous operation. Advances in available computing power have enabled mapping and sensing to approach or achieve real-time operation through, for instance, machine learning (e.g., neural networks). As a result, one application of vision techniques in autonomous driving is providing information about the environment by detecting road signs and/or other signs near a travel route. In addition, vision techniques can also be used to localize the position of a vehicle with respect to known reference marks such as the aforementioned signs. However, despite the noted advances in available computing power, service providers and manufacturers still face significant technical challenges to enable computer vision systems to efficiently recognize and encode features of road signs, such as their edges, shapes, and/or other attributes. This is particularly challenging in computer vision systems that employ advanced neural networks or other similar machine learning systems that include multiple processing nodes.